Social networks often provide group features to help users with similar interests associate and consume content together. Recommending groups to users poses challenges due to their complex relationship: user-group affinity is typically measured implicitly and varies with time; similarly, group characteristics change as users join and leave. To tackle these challenges, we adapt existing matrix factorization techniques to learn user-group affinity based on two different implicit engagement metrics: (i) which group-provided content users consume; and (ii) which content users provide to groups. To capture the temporally extended nature of group engagement we implement a time-varying factorization. We test the assertion that latent preferences for groups and users are sparse in investigating elastic-net regularization. Experiments using data from DeviantArt indicate that the time-varying implicit engagement-based model provides the best top-K group recommendations, illustrating the benefit of the added model complexity.
CT -Personal communication networks (PCNs) based on EEE 802.6 metropolitan area networks (MANS) offer an efficient architecture for distributed management of call and handoff processing functions in support of wireless personal communication services (PCSs). We present a novel reservation arbitrated (RA) access transport method to integrate multimedia and data PCSs over the MAN-based PCNs and discuss how different PCSs can be supported efficiently under the enhanced PCN architecture. We also present analysis and numerical results to demonstrate the significant improvements in system capacity due to the efficiency of the RA transport method.
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